Predicting automatic speech recognition performance using prosodic cues
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Characterizing and recognizing spoken corrections in human-computer dialogue
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Identifying user corrections automatically in spoken dialogue systems
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Towards emotion prediction in spoken tutoring dialogues
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Labeling corrections and aware sites in spoken dialogue systems
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
A comparison of tutor and student behavior in speech versus text based tutoring
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
Exceptionality and natural language learning
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Detecting communication errors from visual cues during the system's conversational turn
Proceedings of the 9th international conference on Multimodal interfaces
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This paper focuses on the analysis and prediction of so-called aware sites, defined as turns where a user of a spoken dialogue system first becomes aware that the system has made a speech recognition error. We describe statistical comparisons of features of these aware sites in a train timetable spoken dialogue corpus, which reveal significant prosodic differences between such turns, compared with turns that 'correct' speech recognition errors as well as with 'normal' turns that are neither aware sites nor corrections. We then present machine learning results in which we show how prosodic features in combination with other automatically available features can predict whether or not a user turn was a normal turn, a correction, and/or an aware site.